{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Surface Water](04.07-Surface Water.ipynb) | [Contents](Index.ipynb) | [5. Statistics](05.00-Statistics.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.8 河道马斯京根演算法\n",
    "\n",
    "使用马斯京根法计算出流量如下，\n",
    "\n",
    "<center>$Q_{j+1}=C_1I_{j+1}+C_2I_j+C_3Q_j$.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(4.16)</center>\n",
    "\n",
    "我们给出了$C_1,C_2,C_3$和$Q_0$的值，并且我们有兴趣从时间(t=0)到(t=19)获得Q的值。在这个例子中，首选我们定义变量，然后使用`for`循环迭代。列表`I`很长，不适合一行。在这种情况下，我们也可以到第二行，开头括号告诉Python列表已经开始，而结尾括号告诉Python这是列表的结尾。我们可以根据需要在任意多行中编写列表，不需要指定任何其他内容来告诉Python列表是多行定义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import numpy as np\n",
    "# 定义变量\n",
    "I = np.array([93, 137, 208, 320, 442, 546, 630, 678, 691, 675, 634, 571, 477,\n",
    "390, 329, 247, 184, 134, 108, 90])\n",
    "C1 = 0.0631\n",
    "C2 = 0.3442\n",
    "C3 = 0.5927\n",
    "\n",
    "Q = np.empty(20) # 定义空数组\n",
    "Q[0] = 85 # Q的初始值\n",
    "\n",
    "# 循环\n",
    "for i in range(1,20):\n",
    "    Q[i] = C1*I[i] + C2*I[i-1] + C3*Q[i-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以使用`matplotlib.pyplot`来绘制入流量和出流量。`-*`表示星形连续线，`--s`表示表示正方形虚线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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CximUUk+NiSj35JcdcaRl5vJ8JztpOmUywR9vg6ev1uhIsahpQ7VaqNFJV+j+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\niJmZ1fZfErBfhT0AnhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59a3dc12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(I, '-*', label='Inflow')\n",
    "plt.plot(Q, '--s', label='Outflow')\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Flow', fontsize=20)\n",
    "plt.legend()\n",
    "plt.savefig('figures/muskingum.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图4.10:入流量和出流量随时间的变化。</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
